Multi-Domain Feature Engineering for Noise-Tolerant Fault Classification in Analog Filter Circuits

Multi-scale wavelet feature fusion with transformer for fault diagnosis of 25 Hz phase-sensitive track circuit

The proposed WT-Transformer fault diagnosis model achieves superior performance in track circuit fault diagnosis, especially in the classification of rare faults with few samples, and the effectiveness of wavelet transformation and time-frequency feature enhancement is verified.

Computational modeling of catastrophic fault classification in Digital to Analog Converters using machine learning

A large part of the cost of producing Digital to Analog Converters (DACs) is related to testing, due to factors such as long time taken for analog fault diagnosis, increased testing time and expensive equipment being required for testing. Therefore, using Machine Learning (ML) based fault classification offers a promising alternative method for testing DACs compared to traditional testing, as it reduces complexity while also improving fault diagnostic accuracy. This study compares the performance of Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Probabilistic Neural Network (PNN) to classify catastrophic faults in both 16-bit Charge Scaling and Binary Weighted DACs that have experienced process variation such as variable threshold voltage, oxide thickness and temperature. Additionally, this paper proposes a framework to eliminate the need for additional test hardware, as the simulated fault signatures are used to develop and validate classifiers for both Charge Scaling and Binary Weighted DACs. The experimental results indicate that the BPNN classified faults with an average classification accuracy of 100% for Charge Scaling DACs and 95.1% for Binary Weighted DACs. Meanwhile, the SVM classified faults with an average classification accuracy of 97.8% for Charge Scaling DACs and 94.09% for Binary Weighted DACs. The proposed PNN achieves significantly better performance with 100% classification accuracy, precision, recall and F1 score for Charge Scaling DAC; 95.5%, 95.1%, 95.5% and 95.3% for Binary Weighted DAC than both SVM and BPNN classifiers. Novelty of this work is the complete benchmarking of the performance of the three classification algorithms for methodically classifying faults in both 16-bit Charge Scaling and Binary Weighted DACs across a range of process variations and thus providing a highly reliable and inexpensive solution for automated DAC fault detection without the need for additional testing apparatus. Not applicable.

Adaptive smoothed soft-threshold method for noise-robust bearing fault diagnosis

Noise-Robust Multi-Domain Feature Integration for Bearing Fault Diagnosis

A hybrid deep learning architecture is proposed for robust vibration-based fault diagnosis in industrial machinery by jointly modeling time-domain, frequency-domain, and temporal dynamics, enabling coherent cross-domain interaction and robust fault characterization.

A Time-Frequency Dual-Domain Multi-Scale Convolutional Neural Network for Bearing Fault Diagnosis under Strong Noise

Experiments on the CWRU bearing dataset across seven signal-to-noise ratio levels demonstrate that the proposed method achieves 99.75% accuracy under clean conditions and maintains 92.50% at -4 dB SNR, representing a 7.25 percentage-point improvement over the single-domain baseline.

Bearing fault diagnosis based on a spectral-guided adaptive multi-scale convolutional network

The results show that SAMACNN outperforms both classical and advanced methods on the two datasets, demonstrating strong robustness and generalization capability in complex variable-condition measurement environments.